The environment is, for example, the surface of a celestial body, another vehicle, a structured relief (city, building), etc.
The carrier must be able to move independently relative to this environment without previous knowledge of it, or with limited previous knowledge. To that purpose, it is necessary to determine its movement relative to this environment.
The carrier's movement is determined using a navigation system comprising one or more navigation sensors installed on board said carrier. The navigation system determines the movement by suitable processing of measurements provided by the navigation sensors. The navigation sensors may be of different types, e.g. GPS receiver, accelerometer, odometer, gyroscope, etc.
The carrier's movement relative to the environment is estimated by utilizing a navigation filter that combines a displacement model (e.g. the equations of the carrier's kinematics) with the navigation measurements provided by the navigation sensors.
However, there are many scenarios in which the navigation system fails to estimate correctly carrier's movement relative to the environment by itself.
This is the case, for example, for a wheeled carrier operating on the surface of a terrain and equipped with an odometer measuring wheel rotation. The odometric measurements are not sufficient to reconstruct the carrier's movement when it slips or slides on the terrain.
It is also the case for a carrier moving relative to a celestial body and having an inertial navigation unit providing measurements of angular speeds and linear accelerations according to at least three independent axes. A navigation filter processes these measurements at high frequency (typically 150 Hz) using a local gravity field model, for example through an extended Kalman filter, in order to reconstruct the carrier's position and speed relative to the celestial body. The inevitable drift of the inertial navigation sensors and the initial error on the carrier's position relative to the environment are sources of errors in the estimate of said carrier's movement, in particular in the estimate of its position relative the environment. These errors accumulate and propagate over time in the subsequent estimates.
This is also the case for a carrier equipped with a GPS receiver. While such a GPS receiver is generally designed to provide an accurate estimate of the carrier's position (typically within one meter in some cases), this accuracy is degraded if the GPS measurements are lost or corrupted. Such a loss or corruption of the GPS measurements can occur in particular because of obstacles on a radio channel between the carrier and the GPS transmitter and/or because of multipath phenomena on said radio channel.
In order to resolve these limitations, it is known to implement a vision system and to equip the carrier with one or more vision sensors that acquire two-dimensional images (2D) of the environment.
Information from the vision system is processed by the navigation system. For that purpose, the navigation filter is augmented to take the measurements provided by the vision system into account.
Many digital implementations of the augmented navigation filter can be developed, for example, Kalman filter, extended Kalman filter, information filter, particle filter, Bayesian filter, etc.
Examples of such augmented navigation systems are, for example, described in the context of space vehicle type carriers in the following scientific publications:    “Navigation for Planetary Approach and Landing”, B. Frapard et al, 5th International ESA Conference on Guidance Navigation and Control Systems, 22-25 Oct. 2002, Frascati, Italy;    “Autonomous Navigation Concepts for Interplanetary Missions”, B. Polle et al, IFAC Symposium on Automatic Control in Aerospace 14-18 Jun. 2004, Saint Petersburg, Russia;    “Mars Sample Return: Optimising the Descent and Soft Landing for an Autonomous Martian Lander”, X. Sembély et al, Symposium on Atmospheric Reentry Vehicles and Systems, 23 Mar. 2005, Arcachon, France;    “Vision Navigation for European Landers and the NPAL Project”, G. Bodineau et al, IFAC Symposium on Automatic Control in Aerospace, 25-29 Jun. 2007, Toulouse, France.
In these scientific publications, an inertial navigation system is considered based on navigation measurements including measurements of the vehicle's linear accelerations along the three axes of a reference frame associated to the vehicle, and measurements of the vehicle's angular speed along these three axes. The inertial navigation filter's state vector comprises states relative to the movement, such as the vehicle's position, speed and attitude angles in a reference frame associated to the environment. The navigation filter propagates (prediction step) an estimate of the state vector taking into account a local gravity field model, and the estimation errors' covariance for these states. The navigation filter recalibrates (update step) the estimate of the state vector, and consequently of the vehicle's movement, from the navigation measurements.
In these scientific publications, the inertial navigation system is combined with a vision system comprising a camera installed in the vehicle, providing 2D images of the environment at a typical frequency of 10 to 100 Hz.
Characteristic areas of the environment are identified in an image. A characteristic area of the environment is an area whose representation in the image, as a set of pixels, has the property of being able to be recognized from one image to the next, for example by image correlation or pattern recognition. For instance, a characteristic area of the image may be a set of several pixels to several tens of pixels, in which there are variations in luminance or texture or contrast in at least two directions.
The characteristic areas of the environment are tracked from one image to the next by image correlation. A point on the image, called the “characteristic point of the image” Mi, is associated with each characteristic area. The characteristic point Mi is, for example, the radiometric or geometric barycenter of the pixel mask representing this characteristic area, or a specific point of this characteristic area. The displacement of characteristic points Mi from one image to the next is representative of the vehicle's translational and rotational movement relative to the environment.
The vehicle's movement in position and attitude is estimated by augmenting the inertial navigation filter's state vector with the coordinates, in a reference frame, of the characteristic points M′i of the environment represented by the characteristic points Mi of the image. A reference frame associated to the carrier is designated by (O,X,Y,Z), and is for example defined by considering 0 to be the center of the camera's focal plane, Z the camera's optical axis, and (X,Y) the camera's focal plane.
The navigation filter's state vector is estimated using 2M measurements corresponding to the directions of the vectors OM′i in the reference frame, in addition to the inertial navigation system's measurements.
The states and measurements of the navigation filter having thus been augmented, the navigation filter's state vector is estimated based on a model of the temporal evolution of the system's states and a model of the various measurements.
However, the main drawback of navigation systems that also use measurements from images lies in the fact that estimating the carrier's state using a navigation filter thus augmented with states and measurements linked to the characteristic points M′i requires significant computation power.
Further, such navigation systems are very sensitive to the quality of the characteristic areas of the environment. In particular, the characteristic areas must be fixed elements of the environment; mistakenly considering a moving element of the environment to be a characteristic area is very detrimental to the accuracy of the estimate of the vehicle's movement.